Subset fusion based T-S fuzzy modeling for blast furnace gas system in steel industry

Blast furnace gas (BFG) is regarded as a very important secondary energy in steel industry, and an effective model to describe the status of BFG system is fairly significant to maintain the system balance and stability. However, the high level noises in industrial data and the disturbances in training samples could lead to the overfitting phenomenon. A fuzzy subset fusion combined with a rule reduction method is proposed in this study to simplify the structure of the rule base and enhance the generalization ability of the fuzzy model. In the proposed method, the parameters of membership functions (MFs) are clustered by using a fuzzy c-means (FCM) method for forming the new representative MFs, and the rules reduction and the consequent parameters update are carried out based on the weights of each rule. The experimental analysis by using a number of real industrial data demonstrates that the proposed method can effectively deal with the fuzzy subset overlapping problem and redundant rules so as to improve the generalization ability of the T-S fuzzy model.